tweedie#
- scikitplot._tweedie.tweedie = <scikitplot._tweedie._tweedie_dist.tweedie_gen object>[source]#
An instance of
tweedie_gen
, providing Tweedie distribution functionality.This instance provides:
probability density function (pdf)
cumulative distribution function (cdf)
random sampling
for the Tweedie distribution.
The Tweedie distribution is part of the exponential dispersion family, characterized by a
p
parameter that determines its behavior:p = 0
: Gaussian distributionp = 1
: Poisson distributionp = 2
: Gamma distributionp = 3
: Inverse Gaussian distribution1 < p < 2
: Compound Poisson-Gamma distribution
- Parameters:
- pfloat
Tweedie power parameter.
- mufloat
Mean or location parameter.
- phifloat
Dispersion parameter, controlling the variance of the distribution.
See also
tweedie_gen
A Tweedie continuous random variable.
Examples
Compute the pdf and cdf at a given point:
>>> import numpy as np >>> from scikitplot.stats import tweedie >>> x = 2.0 >>> pdf_val = tweedie.pdf(x, p=1.5, mu=1, phi=1) >>> cdf_val = tweedie.cdf(x, p=1.5, mu=1, phi=1) >>> pdf_val, cdf_val
(np.float64(0.15640119832636348), np.float64(0.8519363569424107))
Generate random variates:
>>> import numpy as np >>> from scikitplot.stats import tweedie >>> rvs = tweedie.rvs(p=1.5, mu=1, phi=1, size=16) >>> rvs
array([0.02304119, 0.57283985, 0. , 2.37453588, 0.65250069, 0.70390471, 1.15592136, 0.48646911, 0.99747966, 0.25246214, 2.87122138, 0.29726254, 0.47966421, 0.45670358, 1.53907642, 0.04892579])
Plot the pdf over a range:
>>> import numpy as np >>> from scikitplot.stats import tweedie >>> import matplotlib.pyplot as plt >>> x = np.linspace(0, 5, 100) >>> y = tweedie.pdf(x, p=1.5, mu=1, phi=1) >>> plt.plot(x, y, label='Tweedie pdf (p=1.5, mu=1, phi=1)') >>> plt.xlabel("x") >>> plt.ylabel("Density") >>> plt.legend() >>> plt.show()
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Source code
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